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13th International Conference on Information, Intelligence, systems and Applications, IISA 2022

作者： Garrido, Aitor J. Marco, Aitor De La Sen, Manuel Oleagordia, Inigo Garrido, Izaskun University of the Basque Country - UPV/EHU Automatic Control Group - Acg Institute of Research and Development of Processes - Iidp Department of Automatic Control and Systems Engineering Faculty of Engineering of Bilbao Po Rafael Moreno 3 Bilbao48013 Spain University of the Basque Country - UPV/EHU. Automatic Control Group - Ac Advanced Design and Analysis Department Idom Zarandoa Etorb. 23 Bilbao48015 Spain University of the Basque Country - UPV/EHU Automatic Control Group - Acg Institute of Research and Development of Processes - Iidp Department of Electricity and Electronics Faculty of Science and Technology B° Sarriena s/n Leioa48080 Spain

ISBN: (数字)9781665463904

ISBN: (纸本)9781665463904

This paper deals with the upgrade and testing of the of the Ultra-Low Iota Super Elongated Stellarator of the University of the Basque Country. The main upgrades affect to the coil system but also to the microwave source and the data acquisition system. Therefore, it is necessary to update its State-Space model. The updated model is validated by means of experimental output data showing an accurate matching with the real system. Then, in order to test the new model, a Model Predictive control-MPC scheme is successfully implemented both in simulation and experimentally using a real-time control platform. © 2022 IEEE.

关键词： Model predictive control

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Vibration Mitigation of Hybrid Oscillating Water Column-Floating Offshore Wind Turbine Using ANFIS-Based Airflow control 16th

Vibration Mitigation of Hybrid Oscillating Water Column-Floa...

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16th APCA International Conference on automatic control and Soft Computing, controlO 2024

作者： M’zoughi, Fares Aboutalebi, Payam Ahmad, Irfan Rouch, Tahereh Bagheri Garrido, Izaskun Garrido, Aitor J. De La Sen, Manuel Automatic Control Group—ACG Institute of Research and Development of Processes—IIDP Department of Automatic Control and Systems Engineering Faculty of Engineering of Bilbao—EIB/BIE University of the Basque Country—UPV/EHU Po Rafael Moreno no. 3 Bilbao48013 Spain Automatic Control Group—ACG Institute of Research and Development of Processes IIDP Department of Electricity and Electronics Faculty of Science and Technology University of the Basque Country—UPV/EHU Bo Sarriena s/n Leioa48080 Spain

ISBN: (纸本)9783031817236

This paper introduces a novel approach to model and stabilize a Floating Offshore Wind Turbine (FOWT) by employing Oscillating Water Columns (OWC) as an active structural control system. The innovative concept involves designing a new floating barge-like platform with integrated OWCs on opposite sides of the platform to mitigate undesired oscillations of the system. These OWCs counteract the bending forces caused by wind on the tower and waves on the barge platform. To synchronize the opposing forces with the system’s tilting, a proposed Adaptive Neuro-Fuzzy Inference System-based airflow control strategy is employed. Through manipulation of the barge platform’s pitch angle, the ANFIS airflow control system adjusts the valves on either side, opening one and closing the other accordingly. Simulation results, compared with a standard FOWT demonstrate the effectiveness of the ANFIS airflow control. It is shown to be superior in reducing platform pitching and the fore-aft translation of the tower top. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Active Structural control Air Valve Airflow control ANFIS Barge Platform Floating Offshore Wind Turbine Oscillating Water Column

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A Machine-Learning Approach for the Development of a FOWT Model Integrated with Four OWCs

A Machine-Learning Approach for the Development of a FOWT Mo...

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Circuits, systems, Communications and Computers (CSCC), International Conference on

作者： Irfan Ahmad Fares Mzoughi Payam Aboutalebi Izaskun Garrido Aitor Garrido Automatic Control Group-ACG Department of Automatic Control and Systems Engineering University of the Basque Country UPV/EHU Bilbao Spain

ISBN: (纸本)9781665481878

The wind-wave excitations cause structural vibrations on the Floating Offshore Wind Turbines (FOWT) pressing the power generation efficiency and reducing the life expectancy. In particular, tower-top displacement and barge-type platform pitch dynamics are extremely sensitive to wind speed and wave elevation to the point that may even lead to structural instability in extreme conditions. Having into account that computational techniques such as Artificial Neural Networks (ANNs) are widely used in artificial intelligence because of their strong predicting and forecasting capabilities, the aim of this article is to create a deep-layer ANN model that incorporates Oscillating Water Columns (OWCs) into the barge platform. This ANN model enables to address stability issues of the hybrid floating offshore wind platform. The proposed control-oriented model has been successfully validated to achieve adequate dynamic behavior and structural performance using FAST.

关键词： Vibrations Computational modeling Wind speed Artificial neural networks Machine learning Predictive models Aerodynamics

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Multivariable Nonlinear control based on Exact Feedback Linearization with Integral Action for a PMSM

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engineering Letters 2024年第12期32卷 2270-2277页

作者： Velarde-Gomez, Sergio Giraldo, Eduardo Universidad Tecnológica de Pereira Research Group in Automatic Control Pereira Colombia Department of Electrical Engineering Universidad Tecnológica de Pereira Research group in Automatic Control Pereira Colombia

This paper introduces a novel approach for linear quadratic optimal control design for a nonlinear control structure based on a feedback linearization method and applied over permanent magnet synchronous motor (PMSM). The design of the proposed approach is developed by considering rotational speed, direct and quadrature currents as state variables. To achieve accurate tracking of the desired rotational speed, an additional nonlinear controller with integral action is implemented, also employing the exact feedback linearization method, and since the reference for the quadrature current is selected as zero only regulation is required. Consequently, a multivariable nonlinear controller is achieved using exact feedback linearization, offering robustness against external disturbances like torque load. The proposed method is assessed through simulations on a PMSM motor, and the speed reference tracking performance is analyzed with and without integral action. An additional comparison is carried out by incorporating a pole placement technique into the controller design. The proposed exact feedback linearization approach with integral action and linear quadratic control, demonstrates superior performance over other methods in terms of disturbance rejection, control effort and speed tracking. © 2024, International Association of Engineers. All rights reserved.

关键词： Disturbance rejection

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Machine Learning-based OWC Diagnosis Using Real Measured Data from Wave Power Plants

Machine Learning-based OWC Diagnosis Using Real Measured Dat...

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IEEE PES Asia-Pacific Power and Energy engineering Conference (APPEEC)

作者： Fares M’Zoughi Jon Lekube Izaskun Garrido Aitor J. Garrido Automatic Control Group - ACG Department of Automatic Control and Systems Engineering Faculty of Engineering of Bilbao - EIB/BIE University of the Basque Country - UPV/EHU Bilbao Spain Biscay Marine Energy Platform BiMEP Armintza Spain

ISBN: (数字)9798350318098

ISBN: (纸本)9798350318104

The present manuscript introduces a classification for power take-off (PTO) diagnosis in Wave Energy Converter (WEC) farms. The suggested strategy has been tested on the Mutriku Multiple Oscillating Water Column-based wave power plant in order to reduce the Levelized Cost of Energy (LCoE) by implementing predictive maintenance strategies. This has been achieved by employing Linear Discriminant Analysis (LDA) to determine the furthermost relevant features from the measured data. Then the Support Vector Machine (SVM) has been implemented as a classification technique to classify the state of the OWC system.

关键词： Support vector machines Vibrations Training Power measurement Wave power Biological system modeling Predictive models

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Guidance of Agricultural Ground Robots Team with an Aerial Vehicle: A Cost-Effective Solution 56

Guidance of Agricultural Ground Robots Team with an Aerial V...

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56th International Symposium on Robotics, ISR Europe 2023

作者： Ugurlu, Halil Ibrahim Bardakci, Deniz Pham, Huy Xuan Kayacan, Erdal The Department of Electrical and Computer Engineering Aarhus University Aarhus C8000 Denmark Automatic Control Group Department of Electrical Engineering and Information Technology Paderborn University Paderborn Germany

ISBN: (纸本)9783800761418

Increasing the operational efficiency of agricultural machines is essential by the use of artificial intelligence (AI)-based navigation, planning, and control algorithms to handle the increasing demand for food production without compromising sustainability. In this study, an end-to-end path planning algorithm (AgroRL) is proposed for aerial-ground robots team collaboration. In the proposed solution, while main operations in the field are handled by the ground vehicle, the aerial robot is responsible for re-planning a collision-free trajectory for the ground robot when the robot faces an obstacle. Deep reinforcement learning is used for training the end-to-end policy for local re-planning of the aerial robot. The agent, informed by the global trajectory, generates local plans based on depth images. Variational autoencoders are also investigated for dimension reduction of the depth images in obstacle avoidance context to speed up deep reinforcement learning and alleviate the computational complexity of the policy network. The agriculture environment is developed in the Webots open-source robot simulator for training and testing purposes. The efficiency and efficacy of the end-to-end planner are evaluated over a number of cluttered field scenarios. The simulation experiments demonstrate a single aerial vehicle guiding multiple ground robots in agricultural operations. © VDE VERLAG GMBH Berlin Offenbach.

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Adaptive Neural Network Identification for Robust Multivariable systems

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IAENG International Journal of Applied Mathematics 2024年第1期54卷 68-76页

作者： Osorio-Arteaga, Felipe Giraldo, Eduardo Department of Electrical Technology Universidad Tecnológica de Pereira Research Group in Automatic Control Pereira Colombia Department of Electrical Engineering Universidad Tecnológica de Pereira Research group in Automatic Control Pereira Colombia

This paper proposes a robust identification and control-based on a neural network method for a Twin Rotor Multivariable System (TRMS) using a recursive adaptive training algorithm. The algorithm is based on a recursive least squares approach with an additional steepest descent stage. An Adaline neural network is used for modeling the system, and a robust structure is selected based on a linear auto-regressive structure with exogenous inputs (ARX) related to the estimation error. The identification is performed online and the system is controlled under a polynomial structure by pole placement with a dead-beat strategy. The method is evaluated in terms of estimation and tracking error in the presence of external additive disturbances, parametric disturbances, and sinusoidal reference signals. The Root-Mean Square Error (RMSE) is used to evaluate the estimation performance and the Integral-Time Absolute Error (ITAE) is used to evaluate the tracking performance. As a result, a novel robust controller based on a neural network is designed where the best results are obtained for a training recursive least squares algorithm with an additional steepest descent stage. © 2024, International Association of Engineers. All rights reserved.

关键词： Multivariable systems

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Mitigating Undesirable Oscillatory Behavior in Barge-type Floating Offshore Wind Turbines through Oscillating Water Columns

Mitigating Undesirable Oscillatory Behavior in Barge-type Fl...

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IEEE PES Asia-Pacific Power and Energy engineering Conference (APPEEC)

作者： Payam Aboutalebi Dong Trong Nguyen Aitor J. Garrido Izaskun Garrido Department of Automatic Control and Systems Engineering Engineering School of Bilbao - EIB/BIE Automatic Control Group - ACG University of the Basque Country - UPV/EHU Bilbao Spain Department of Marine Technology Norwegian University of Science and Technology (NTNU) Trondheim Norway

ISBN: (数字)9798350318098

ISBN: (纸本)9798350318104

This paper addresses the challenge of reducing undesired oscillations in barge-type floating wind turbines (FWTs) caused by wind and wave forces, which result in fluctuations in generator power and system oscillations. These oscillations can negatively impact the system’s lifespan, power generation, maintenance costs, and component stress. To mitigate these issues, oscillating water columns (OWCs) are integrated within the FWT substructure. A control strategy is proposed for the OWCs based on response amplitude operators, aiming to minimize oscillations in a 5 MW wind turbine structure. A considered case study is conducted for above-rated wind speeds to evaluate the effectiveness of the controllers and to compare the performance of an uncontrolled FWT against a controlled FWT integrated with OWCs. The findings demonstrate the efficacy of the control strategy in pitch oscillations reducing for 29.9% and generated power fluctuations reducing for 23.03% and highlight the advantages of integrating OWCs in floating offshore wind turbines.

关键词： Vibrations Fluctuations Wind speed Switches Wind power generation Valves control systems

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Visual Tracking Nonlinear Model Predictive control Method for Autonomous Wind Turbine Inspection 21

Visual Tracking Nonlinear Model Predictive Control Method fo...

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21st International Conference on Advanced Robotics, ICAR 2023

作者： Amer, Abdelhakim Mehndiratta, Mohit Le Fevre Sejersen, Jonas Pham, Huy Xuan Kayacan, Erdal Department of Electrical and Computer Engineering Aarhus C8000 Denmark Gim Robotics Espoo Finland Automatic Control Group Paderborn University Department of Electrical Engineering and Information Technology Paderborn Germany

ISBN: (纸本)9798350342291

Automated visual inspection of on- and off-shorewind turbines using aerial robots provides several benefits, namely, a safe working environment by circumventing the need for workers to be suspended high above the ground, reduced inspection time, preventive maintenance, and access to hard-to-reach areas. A novel nonlinear model predictive control (NMPC) framework alongside a global wind turbine path planner is proposed to achieve distance-optimal coverage for wind turbine inspection. Unlike traditional MPC formulations, visual tracking NMPC (VT-NMPC) is designed to track an inspection surface, instead of a position and heading trajectory, thereby circumventing the need to provide an accurate predefined trajectory for the drone. An additional capability of the proposed VT-NMPC method is that by incorporating inspection requirements as visual tracking costs to minimize, it naturally achieves the inspection task successfully while respecting the physical constraints of the drone. Multiple simulation runs and real-world tests demonstrate the efficiency and efficacy of the proposed automated inspection framework, which outperforms the traditional MPC designs, by providing full coverage of the target wind turbine blades as well as its robustness to changing wind conditions. The implementation codes11https://***/open-airlab/VTNMPC-Autonomous-Wind-Turbine-Inspection are open-sourced. © 2023 IEEE.

关键词： Inspection

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Enhancing Stability and Performance of Hybrid Offshore Wind Platforms: A Novel Fuzzy Logic control Approach with Computational Machine Learning

Enhancing Stability and Performance of Hybrid Offshore Wind ...

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International Conference on control, Mechatronics and Automation (ICCMA)

作者： Irfan Ahmad Fares M'zoughi Payam Aboutalebi Izaskun Garrido Aitor J. Garrido Automatic Control Group - ACG Department of Automatic Control and Systems Engineering Engineering School of Bilbao - EIB/BIE University of the Basque Country - UPV/EHU Bilbao Spain Department of Marine Technology Norwegian University of Science and Technology (NTNU) Moholt Trondheim Norway

Harnessing the power of wind and waves for renewable energy production has become vital in the quest for sustainable electricity generation. The fusion of Floating Offshore Wind Turbines (FOWTs) and Oscillating Water Columns (OWCs) has introduced a groundbreaking concept of hybrid offshore platforms, offering immense potential for energy absorption, reduced dynamic response, load mitigation, and improved cost efficiency. The primary goals of this study revolve around two key objectives: (i) the development of a regression-based modeling technique for the hybrid aero-hydro-elastic-servo-mooring coupled numerical system, and (ii) the implementation of a customized fuzzy-based control mechanism to ensure platform stability. To achieve these objectives, computational Machine Learning (ML) tools, specifically Artificial Neural Networks (ANNs), are utilized to replicate the behavior of the detailed numerical model of the FOWTs integrated with OWCs. Subsequently, a Fuzzy Logic control (FLC) scheme is employed to establish a structural controller that effectively mitigates unwanted vibrations. The experimental results confirm the potential of ANN-based modeling as a simpler yet effective alternative to complicated nonlinear NREL-5MW FOWT dynamical models. Furthermore, the use of the FLC system enhances platform stability in a variety of wind and wave conditions.

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